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Related Experiment Video

Updated: Feb 19, 2026

Comprehensive Analysis of Transcription Dynamics from Brain Samples Following Behavioral Experience

Comprehensive Analysis of Transcription Dynamics from Brain Samples Following Behavioral Experience

Published on: August 26, 2014

Characterising processing conditions that artifactually bias human brain tissue transcriptomes.

Moein Yaqubi¹, Michael Thomas^2,3, Jonathan Talbot-Martin^2,3

¹Neuroimmunology Unit, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada.

Nature Communications

|February 17, 2026

Summary

This summary is machine-generated.

Autopsy brain tissue analysis reveals gene signature changes due to post-mortem handling. We developed a predictive tool, TTRUTH (Time and Temperature Response genes Underlying Transcriptional Heterogeneity), to standardize brain transcriptomic data.

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Published on: November 13, 2011

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Last Updated: Feb 19, 2026

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Non-Laser Capture Microscopy Approach for the Microdissection of Discrete Mouse Brain Regions for Total RNA Isolation and Downstream Next-Generation Sequencing and Gene Expression Profiling |

Non-Laser Capture Microscopy Approach for the Microdissection of Discrete Mouse Brain Regions for Total RNA Isolation and Downstream Next-Generation Sequencing and Gene Expression Profiling |

Published on: November 13, 2011

Area of Science:

Neuroscience
Genomics
Bioinformatics

Background:

Autopsy brain tissue is vital for neurobiology research.
Post-mortem handling introduces transcriptional artifacts, complicating data interpretation.
Standardized methods are needed to account for tissue degradation over time and temperature variations.

Purpose of the Study:

To characterize transcriptomic changes in human brain tissue based on post-mortem intervals (PMIs).
To identify and define artifact-responsive genes, termed Brain Artifact Genes (BAGs).
To develop a predictive signature (TTRUTH) for quantifying processing effects in brain RNA-seq data.

Main Methods:

Compared transcriptomic signatures of immediately extracted brain tissue with short (~6h) and long (~36h) PMIs.
Mapped artifact gene signatures onto single-nucleus RNA-seq data.
Utilized deep learning to create the TTRUTH predictive signature.

Main Results:

Significant gene signature deviations (BAGs) were observed in both short and long PMIs compared to immediate extraction.
Glutamatergic neurons showed early induction of artifact genes, followed by oligodendrocytes.
The TTRUTH signature effectively quantifies processing-related transcriptional heterogeneity.

Conclusions:

Post-mortem time and temperature significantly impact brain transcriptomic data.
The TTRUTH signature provides a tool to standardize and interpret autopsy-derived brain RNA-seq datasets.
This work enhances data reliability for neurobiological research and sample stratification.